Corrections: Breast cancer screening guidelines for young women of color
Bibliographic record
Abstract
We enjoyed reading Hendrick et al's important article entitled “Age Distributions of Breast Cancer Diagnosis and Mortality by Race and Ethnicity in US Women”1 and the accompanying editorial by Yaffe.2 We write, however, to point out several important errors in the editorial by Seewaldt and Bernstein.3 They incorrectly cite Stapleton et al4 as stating that “for Black, Asian, and Hispanic/Latina women, the diagnosis of invasive breast cancer peaked at the age of 40 years (vs the mid-60s for NH-White women).” The actual statement is that “the median age at diagnosis was 59 years for White (IQR, 51-67 years), 56 years for Black (IQR, 49-65 years), 55 years for Hispanic (IQR, 48-64 years), and 56 years for Asian patients (IQR, 48-64 years) (Figure 1).” Figure 1 shows that the “peak” is in the mid to late 40s for Women of Color. Citing an advocacy website,5 they also incorrectly state that “all 50 states in the United States have enacted legislation requiring radiologists to inform women of all races and ethnicities who have high breast density that they 1) are at increased breast cancer risk and 2) may benefit from supplemental breast cancer screening modalities, such as whole breast screening ultrasound.” The federal law passed in 2019 ensured that the Food and Drug Administration process of updating postmammography reporting requirements for both patients and referring physicians would move forward. To date, the Food and Drug Administration has not introduced a federal standard.6 Individual state “inform” requirements are still accomplished through individual state laws. Currently, 38 states and the District of Columbia7 have active density inform laws, but they vary in the depth and breadth of information required to be provided to women. For instance, not all mention increased risk or supplemental screening or even require mammography facilities to inform a given woman that she has dense breasts. The outcomes of breast cancer in women with dense breasts are, in fact, worse in the cited analysis of Gierach et al8 with an excess of late-stage (II and III) disease. The relatively short mean follow-up of 6.6 years was insufficient for an accurate analysis of mortality after screening. The primary intent of dense breast notification is to address the risk of underdiagnosis from mammography: cancer, if present, could be masked. This information is intended to allow shared decision-making with a woman's health care provider regarding possible supplemental screening with magnetic resonance imaging (MRI) or ultrasound. The added yield from MRI, averaging 10 to 16 cancers per 1000 women screened,8-10 far exceeds that from ultrasound at 2 to 3 per 1000,11-13 but MRI is not available for all women with dense breasts at this time. As Monticciolo et al14 have pointed out, a risk assessment should be performed for all women by the age of 30 years so that women at high risk can begin screening with MRI if that is appropriate. Women of Ashkenazi Jewish heritage and Women of Color are especially encouraged to seek a formal risk assessment by the age of 30 years, possibly including testing for pathogenic mutations. No specific funding was disclosed. Paula B. Gordon has received honoraria for speaking at university-sponsored and society continuing medical education events but always donates them to Dense Breasts Canada; she previously served as a secretary/treasurer for the Society of Breast Imaging, volunteers on the medical advisory boards of Dense Breasts Canada and DenseBreast-info.org, and is a stockholder of Volpara Solutions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.143 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.015 | 0.023 |
| Insufficient payload (model declined to judge) | 0.032 | 0.034 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".